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GIS Workshops: Mapping Texas Ozone

Fall 2012 Workshop: Mapping Ozone Levels in Texas

   

Objectives

Learn how Geographic Information Systems and ESDA (Exploratory Spatial Data Analysis) can help to estimate ozone levels across the state.  In addition to ESDA, this workshop will cover the basics of kriging, a popular interpolation method.  Open to all. No GIS or statistical background required.

 

During this workshop, participants will:

  • Calculate ozone contours (ppb, parts per billion)
  • Calculate ozone concentrations within a specified radius from their home
  • Export results to Google Earth

 

Library books on kriging

Workshop Resources

Data Sources Used

Workshop Plan

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Before Analysis: Explore Data

  • Navigate to the C:/PollutionModelling directory in Windows and launch (double-click) Ozone.mxd.  Within ArcMap, we will explore each layer's Attribute Table and Symbology.

Step #1: Geocode Home Address

    • This step will plot a point at any address in Texas so we can analyze localozone concentrations after interpolating.
      • Click (1) Geocode Address on the toolbar
      • Select the Locations tab
      • Type any address in Texas
      • Click Find


    • Right-click the top result, and select Add Point

Step #2: Enable Extensions

  • This workshop requires both (1) Geostatistical Analyst and (2) Spatial Analyst.  Ensure both are checked.

Step #3: Join Ozone Concentrations

  • This step will Join a specific month's hourly ozone concentrations to the Air Monitoring Sites.
    • Click (3) Join Ozone Concentrations.
    • Under Ozone Table, use the drop-down menua nd select Jul$.
      • We can use any month, but for the purposes of this first iteration, let's all use the same month.
    • Under Output File, leave the default.
      • If you have a red X, change the file name as this must not be the first time you are running this.

Step #4: Geostatistical Analyst
(The big step)

  • This step has three parts: (1) Perform quick kriging using defaults, (2) Exploratory Spatial Data Analysis (ESDA), and (3) Rerun kriging removing trends.

Step #4A: Initial Kriging

  • Click (4) Geostatistical Analyst/Geostatistical Wizard
    • Kriging Step 1/5
      • Under Methods select Kriging/Cokriging
        • Under Input Data, and under Dataset:
        • Source Dataset: ozoneMonitors
        • Data Field: JUL__OZ_12

    • Next
  • Kriging Step 2/5
    • Under Kriging Type, select Ordinary.
    • Under Output Type, select Prediction.
      • Next
    • Kriging Step 3/5: Semivariogram/Covariance Modelling
      • Semivariogram Values: Difference squared of the ozone measurements taken at pairs of sampling locations separated by different distances.
      • This allows us to examine spatial relationships between measured points.
        • Blue line is the model
        • Red dots represent grouped pairs of points
      • Three important terms:
        • Nugget: Variability in the field data that cannot be explained by distance between the observations
        • Sill: Maximum observed variability in the data
        • Range: Point at which the semivariance stops increasing
        • Ideally, we want a small nugget and a large sill.

    • Next
  • Kriging Step 4/5: Searching Neighborhood
    • Click on the image to see which samplpe points are contributing to the predicted value.
    • Expand Weights to see the legend.
    • Next
  • Kriging Step 5/5
    • To judge if a model provides accurate predictions, verify that:
      • The predictions are unbiased, indicated by a mean prediction error close to 0.
      • The standard errors are accurate, indicated by a root-mean-square standardized prediction error close to 1.
      • The predictions do not deviate much from the measured values, indicated by root-mean-square error that is as small as possible.

  • FINISH!

Step #4B: ESDA

  • We will take a look at the (1) Histogram, (2) Semivariogram/Covariance Cloud, and the (3) Trend Analysis accessible via (4) Geostatistical Anslyst/Explore Data.
    • Histogram
      • Ensure Attribute is set to JUL__OZ_12.
      • Click a bar to see the corresponding coordinates on the map.
        • Definitely a southern pgrogression.  I wonder why?

  • Semivariogram/Covariance Cloud
    • Ensure Attribute is set to JUL__OZ_12.
    • X-axis shows the distance between pairs of points and Y-axis shows the variance
      • We can see pairs of points with smaller distance have smaller variation, which is good.
  • Trend Analysis
    • Trend is a non-random component that can be represented by mathematics.
    • Blue Line - North-South
    • Red Line - East West
    • * The trend lines are U-shaped, not linear.  We need to ensure our kriging model follows this shape.
  • Polynomial Examples:
    • First Order
    • Second Order
    • Third Order
  

Step# 4C: Kriging Again

  • Repeat Step 4A, this time selecting 2nd order polynomial as the Trend.
  • Compare diagnistic statistics.

Step# 5: Buffer Home Address

  • Click (5) Buffer.

  • Click Next

  • Ensure the Specified Distance and Units are set to 10 Miles.
  • Click Next

  • Hit the browse button, change the type to Shapefile
  • Save

Step# 5: Buffer Home Address

  • This step will calculate the average ozone concentration within the 10-mile radius around our address
    • Input geostatistical layer: The kriging layer generated with the trend removal.
    • Input raster or feature zone data: Your buffer
    • Output table: Seelct a suuitable location/file name

Please Evaluate Before You Leave =)